Machine Learning for Imbalanced Data: Tackle imbalanced datasets using machine learning and deep learning techniques

Take your machine learning expertise to the next level with this essential guide, utilizing libraries like imbalanced-learn, PyTorch, scikit-learn, pandas, and NumPy to maximize model performance and tackle imbalanced dataKey Features Understand how to use modern machine learning frameworks with detailed explanations, illustrations, and code samples Learn cutting-edge deep learning techniques to overcome data imbalance Explore different methods for dealing with skewed data in ML and DL applications Purchase of the print or Kindle book includes a free eBook in the PDF format Book DescriptionAs machine learning practitioners, we often encounter imbalanced datasets in which one class has considerably fewer instances than the other. Many machine learning algorithms assume an equilibrium between majority and minority classes, leading to suboptimal performance on imbalanced data. This comprehensive guide helps you address this class imbalance to significantly improve model performance.Machin